Integrated Deprived Area
Mapping System

A research network working to improve our current approaches to “slum” area mapping.

We are a global network of slum mappers

Projects in the IDEAMAPS Network generate citywide surface maps of area deprivations and assets, and support stakeholders to use data for upgrading, advocacy, monitoring, and more.

We strive for data to be validated by city stakeholders, comparable across cities, updated routinely, and usable by community and local government stakeholders to seek equity and justice for all.

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Our mapped outputs

IDEAMAPS Network surface maps cover city administrative boundaries and larger built-up metropolitan areas to ensure that diverse users can access complete data for the city, however “the city”is defined.

Data outputs are Data outputs are formatted as ~100x100m grid cells to serve multiple purposes:formatted as ~100x100m grid cells to serve multiple purposes:

  1. To protect vulnerable populations from fines, harassment, or eviction by avoiding the delineation of exact “slum” boundaries
  2. To accurately represent cities as interconnected, interdependent continuums of people and places.
  3. To enable results to be aggregated into larger user-relevant areal unit such as wards or health service areas.
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Integrating approaches to slum mapping

The IDEAMAPS Network co-designs new data and new processes of data exchange and integration by leveraging the strengths of often silo-ed “slum” mapping traditions.

We center the context knowledge of community mappers and other field experts; use scalable, reproducible modelling techniques as a vehicle; and integrate diverse social and environmental data from communities, local and national governments, NGOs, academia, and the private sector.

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2022 - 2025
Currently Active
IDEAMAPS Data Ecosystem

LAGOS, Nigeria - NAIROBI, Kenya - KANO, Nigeria

To co-create an integrated data ecosystem that enables routine, accurate mapping of slums, informal settlements, and other deprived areas across LMIC cities 

Currently Active

Mexico City, Mexico - Medellín, Colombia - Salvador, Brazil - Buenos Aires, Argentina - Lagos, Nigeria - Nairobi, Kenya - Mumbai, India - Jakarta, Indonesia

The primary objective of IDEAtlas is to develop, implement, validate and showcase advanced AI-based methods to automatically map and characterize the spatial extent of slums from Earth Observation (EO) data.

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